CN113361997A - Dangerous waste transportation path real-time planning method based on risk minimization - Google Patents
Dangerous waste transportation path real-time planning method based on risk minimization Download PDFInfo
- Publication number
- CN113361997A CN113361997A CN202110624298.3A CN202110624298A CN113361997A CN 113361997 A CN113361997 A CN 113361997A CN 202110624298 A CN202110624298 A CN 202110624298A CN 113361997 A CN113361997 A CN 113361997A
- Authority
- CN
- China
- Prior art keywords
- risk
- road
- path
- dynamic
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013439 planning Methods 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 45
- 239000002699 waste material Substances 0.000 title claims abstract description 26
- 230000007613 environmental effect Effects 0.000 claims abstract description 23
- 230000008569 process Effects 0.000 claims abstract description 19
- 239000002920 hazardous waste Substances 0.000 claims description 43
- 230000003068 static effect Effects 0.000 claims description 25
- 238000011156 evaluation Methods 0.000 claims description 21
- 238000004364 calculation method Methods 0.000 claims description 18
- 238000012937 correction Methods 0.000 claims description 17
- 206010039203 Road traffic accident Diseases 0.000 claims description 15
- 230000001133 acceleration Effects 0.000 claims description 7
- 231100001261 hazardous Toxicity 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 238000012986 modification Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims description 3
- 241000764238 Isis Species 0.000 claims 2
- 230000006399 behavior Effects 0.000 abstract description 13
- 238000012502 risk assessment Methods 0.000 abstract description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000013077 scoring method Methods 0.000 description 2
- 238000012384 transportation and delivery Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 239000003651 drinking water Substances 0.000 description 1
- 235000020188 drinking water Nutrition 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 239000000383 hazardous chemical Substances 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 230000002458 infectious effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000002285 radioactive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 231100000331 toxic Toxicity 0.000 description 1
- 230000002588 toxic effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the application provides a method for planning a dangerous waste transportation path in real time based on risk minimization, risk factors are brought into an optimal path planning process, dangerous waste vehicles, road parameters, environmental parameters, traffic information and driving behaviors are comprehensively considered, a transportation risk assessment function model is built, the risk of dangerous waste transportation is evaluated in a refined mode, and the planned path is more comprehensive and objective.
Description
Technical Field
The application relates to the field of hazardous waste transportation, in particular to a method for real-time planning of a hazardous waste transportation path based on risk minimization.
Background
Hazardous waste is flammable, explosive, highly corrosive, toxic, infectious, radioactive, etc., and thus it is easy to cause sudden or cumulative environmental risks, and the prevention of the environmental risks of hazardous waste is one of the important targets of hazardous waste management. From the perspective of the whole-process management of hazardous waste, the transportation of the hazardous waste causes the movement of a risk source, safety accidents in the transportation process may cause leakage and explosion of hazardous substances, casualties, property loss, and pollution to the atmosphere, water environment and soil in the vicinity and in the influence range or diffusion range, and risk receptors may generate differences due to different transportation paths, and unreasonable planning of the transportation paths may cause the increase of risk probability and sensitivity of the risk receptors.
Because the production nature, the place of the unit of producing waste and the hazardous waste variety that produces are various, and distribution density is low, and the route from the unit of producing waste to the unit of handling is various, and the distance is longer, and the route area is numerous, and the transport route of hazardous waste vehicle has characteristics such as randomness and ambiguity, has led to the uncertain higher of risk transportation link.
In the prior art, the method for planning the optimal path of the transportation of the hazardous waste has the following defects: 1, the safety of path planning is insufficient, most methods directly obtain the shortest path based on time, distance and cost, risk factors are not considered, and effective and comprehensive dangerous waste transportation risk evaluation methods and means are lacked; 2, the real-time performance of path planning is low, when the environment or scene changes or sudden accidents occur, the dynamic adjustment cannot be rapidly carried out, and the optimal path guidance cannot be timely and effectively provided for the dangerous waste transport vehicle; 3, the updating frequency of the basic data of the path is too low, so that the path planning result has hysteresis and the optimal path cannot be output in time; and if the acquisition and update frequency of the real-time information is set to be too high, the data calculation amount is large, the operation load of the remote processor is high, and the path planning cannot be efficiently and accurately carried out.
Therefore, reasonably planning the transportation path of the hazardous waste by comprehensively considering the risk occurrence probability and the risk result is an important basic task for ensuring the risk minimization in the transportation process of the hazardous waste.
Disclosure of Invention
The application provides a dangerous waste transport path real-time planning method based on risk minimization, which aims to solve the problems of insufficient safety, low real-time performance and lagging path planning result of dangerous waste transport path planning in the prior art.
A method for risk minimization based real-time planning of hazardous waste transport paths, the method comprising:
and 6, determining a dynamic correction coefficient of the probability of the occurrence of the risk accident according to the dynamic parameters, importing the dynamic parameters on the basis of the initial risk evaluation function model, constructing a dynamic risk evaluation function model, calculating a real-time dynamic risk value of each road section, and performing real-time path planning weighting by adopting a Dijkstra algorithm so as to update the optimal planning path.
For convenience of description, links between adjacent nodes may be numbered and referred to as a link set J ═ 1, 2, 3, … …, J }.
The risk value can be a numerical value, and all road sections can obtain unitless numerical values corresponding to all road sections based on the same standard; of course, the risk value may also have units.
Therefore, the initial risk value is determined according to the static parameters so as to screen the initial optimal path, the dynamic updating frequency of the real-time planning path information is set according to the initial risk value, and whether the initial optimal path is optimized and adjusted to the real-time optimal path planning or not is determined by combining the dynamic parameter information, so that the optimal path for the transportation of the hazardous waste is determined. Therefore, when the path is planned, not only are static parameters and dynamic parameters considered, risk minimization can be comprehensively realized, but also the determination of dynamic updating frequency based on the initial risk value is considered, so that the accuracy and the real-time performance of path planning can be ensured; in addition, the dynamic updating frequency is set, the data calculation amount can be reduced, the running load of the remote processor is effectively relieved, the frequent updating of the path is avoided, and the scientificity and the high efficiency of the path planning are ensured.
The starting node is the starting point of the planned path, and the target node is the end point of the planned path. Based on different dangerous waste types and transportation purposes, the identification process of the starting node and the target node is as follows: for example, when dangerous waste is treated, when a transport vehicle does not start, a dangerous waste generation unit is regarded as a starting point of a planned path and is used as a starting node; the dangerous waste disposal unit is regarded as the end point of a planned path and is used as a target node; and if the transport vehicle leaves the starting node, when the transport vehicle is in the transportation process, the nearest intermediate node to be reached is changed into the starting point of a new planned path when the dynamic risk evaluation function model is constructed, the intermediate node is used as the starting node, the target node is kept unchanged, and the new planned path is kept the same as or different from the road section of the original planned path according to the result of real-time dangerous waste transportation risk evaluation.
Optionally, the step of selecting an intermediate node of the planned path includes: and acquiring the quantity and position information of the intersections based on the electronic traffic map, and taking each intersection as an intermediate node. Regarding the selected area range of the intermediate node, the selected area is the area of a road network in the administrative area where the starting node and the target node are located; specifically, the administrative region scope may be determined according to the positions of the starting node and the target node. For example, when the start node and the target node are located in the same area, the intermediate node may be selected as all intersections of the road network in the area; when the starting node and the target node are positioned in the same city and different areas, the intermediate node can be selected as all intersections of the road network in the city; of course, the selected area range of the intermediate node may also be set by other methods than administrative area division according to actual requirements.
The intersection is selected as a turning point capable of changing a path, for example, a traffic light intersection in a city or an intersection without a traffic light, an exit and entrance of a highway, an intersection of an urban and rural road, an intersection of a town road and the like can be selected.
Therefore, the selection process of the intermediate node is very simple and clear, and the user does not need to set an additional function to identify and select the intermediate node by himself or herself, but automatically identifies and determines according to the loaded latest electronic traffic map.
In the above, the static parameters refer to information irrelevant to the real-time transportation condition of the vehicle, that is, the parameters are basically kept unchanged in the whole transportation process; the dynamic parameters are information related to the real-time transport condition of the vehicle, and the parameters change in real time during the transport process.
Optionally, the static parameter information includes: information of hazardous waste transport vehicles, road information, and environmental information.
Therefore, when the static risk evaluation function model is established, not only the environmental information but also the information of the hazardous waste transport vehicle and the road information are considered, wherein the probability of the hazardous accident of the transport vehicle on the corresponding road section can be calculated based on the environmental information, and the accident consequence caused after the hazardous accident can be calculated based on the information of the hazardous waste transport vehicle and the road information. The initial risk value of each road section is calculated by combining the accident probability and the accident consequence, so that the calculation of the initial risk value is more comprehensive, and the path planning with minimized risk is facilitated.
Optionally, the road information includes at least one of a length of a road segment, a road type, a number of lanes, a road width, a historical traffic accident occurrence rate, and road restriction information, where the road restriction information refers to information of road restrictions such as a road speed limit and a road restriction. It is understood that the road information may have more parameters, and the more the parameters are, the more the initial risk value can be calculated by the parameters, and as an embodiment, the road information includes the length of the road section, the type of the road, the number of lanes, the road width, the historical traffic accident occurrence rate, and the road limit information.
Optionally, the information of the hazardous waste transport vehicle comprises at least one of a category and hazardous characteristics of the carrying hazardous waste, and a maximum load of the vehicle. It will be appreciated that the information about the hazardous waste transport vehicle, including the type and hazardous nature of the hazardous waste carrying vehicle, and the maximum load of the vehicle, may have more parameters, and that more parameters may be advantageous in accurately calculating the initial risk value from these parameters, as an embodiment.
Optionally, the environmental information includes at least one of weather, temperature, population density, and environmental restriction information, where the environmental restriction information refers to information of environmental restrictions in sensitive areas (such as drinking water source areas, rivers, lakes, forests, schools, historical historic sites, business districts, scientific research bases, stations, and the like). It is understood that more parameters of the environmental information may be provided, and that more parameters may be provided to facilitate accurate calculation of the initial risk value from the parameters, including weather, temperature, population density, and environmental restriction information, as one embodiment.
Optionally, the initial risk value is denoted as Rj，RjThe calculation formula of (a) is as follows:
Rj＝F(Pj,Qj) (1)
in the formula (1), PjProbability of occurrence of a risk accident for road section j, QjAs a consequence of an accident for section j;
in the formula (1), RjIs PjAnd QjSo that the initial risk value RjIs to combine accident probability and accident consequenceIs calculated such that an initial risk value R is obtainedjThe calculation is more comprehensive, thereby being beneficial to realizing the path planning with minimized risk.
Optionally, the P isjAnd QjIs defined as follows:
Pj＝f(α,l,d,n,w,p) (2)
in the formula (2), alpha is a dynamic correction coefficient, l is the length of a road section, d is a road type, n is the number of lanes, w is a road width, and p is a historical traffic accident occurrence rate;
in the formula (3), r is the danger level of dangerous waste, T is the weather condition, K is the temperature, rho is the human mouth density, and mu is other environmental influence factors;
regarding the dynamic correction coefficient α, when the transport vehicle is not started yet, the transport vehicle is located at the start node at this time, and the dynamic correction coefficient α takes a fixed value of 1, that is, since the vehicle is in a stationary state to be started at this time, the dynamic correction coefficient α directly takes a fixed value of 1 without considering the dynamic parameters; and if the transport vehicle is in the transportation process, the dynamic correction coefficient alpha obtained based on the dynamic parameters needs to be considered when the dynamic risk evaluation function model is reconstructed.
In respect of PjAnd QjThe specific calculation method of the function can be based on an expert scoring method and a historical accident situation database, index weights of all parameters are distributed, and then weighting calculation is carried out.
For example,
Pj＝αj×l×pd,n,w；
Qj＝0.3r+0.15T+0.15K+0.25ρ+0.15μ；
of course, P abovejAnd QjIn the calculation method of the function, specific parameter selection and parameter weight need to be determined by combining actual transportation information of the hazardous waste.
The above-mentioned pair accident probability PjIn the definition formula (2), the parameters are introduced comprehensivelyThe length of the section j, the road type, the number of lanes, the road width, and the historical traffic accident occurrence rate. For accident QjThe parameters comprehensively introduced into the definition formula comprise the danger level of the dangerous waste, the weather condition, the temperature, the population density and other environmental influence factors.
Optionally, the step of performing preliminary path planning weighting by using Dijkstra algorithm, and screening the initial optimal path includes:
step 2-2-1, the initialization set S contains only the start node c0The set E contains the remaining nodes, R [0 ]][k]Is c0To ckA path risk value of (a);
step 2-2-2, initialize the start node c0To node c in set EkPath risk value dist k]＝R[0][k]If the nodes are not connected, dist [ k ]]＝∞；
Step 2-2-3, the minimum path risk value dist [ kmin [ ]]Corresponding node ckminMoving from the set E to the set S;
step 2-2-4, update the initial node c0To node c in set EkPath risk value of, dist k]＝min{dist[k],dist[kmin]+R[kmin][k]}；
Step 2-3-5, if the target node is added into the set S, the optimal path is the adding sequence of the nodes in the set S; otherwise, go to step 2-2-2.
In step 2-2-2, the fact that the nodes are not connected means that the two nodes cannot directly pass through, and the judgment is based on the fact that some limiting information includes road limiting information, environment limiting information and the like, for example, when the road is limited, the nodes are in a typical situation of being not connected.
In this way, by using the calculation model of Dijkstra algorithm and adopting the risk value of each road section as a weighted object, the initial optimal path with minimized risk can be obtained. The Dijkstra algorithm is a typical single-source shortest path algorithm, and is used for calculating the shortest path from one node to all other nodes, and the path distance is converted into a risk value in the application, so that a path with minimized risk is solved.
Optionally, the step of performing the road segment risk classification according to the initial risk value includes: presetting a dangerous waste transportation risk threshold; and comparing the initial risk value with the risk threshold value, so as to carry out risk classification on each road section.
Wherein the risk threshold may be selected via a historical accident scenario database.
Therefore, after the risk threshold is preset, the initial risk value is compared with the risk threshold due to the fact that the risk threshold is derived from historical accident statistical data, the risk level of the initial risk value can be determined, the specific risk value corresponds to the corresponding risk level, and the risk value can be classified and identified conveniently.
Optionally, the preset hazardous waste transportation risk threshold value; comparing the initial risk value with the risk threshold value, so as to perform risk classification on each road segment, wherein the step of performing risk classification on each road segment comprises the following steps:
step 3-1, presetting a dangerous waste transportation risk threshold value
And
step 3-2, obtaining the initial risk value R of the road section j according to the step 2jCarrying out risk classification on each road section, and inputting a risk grade result into a road section set J; if it is
Classification as low risk; if it is
Is a medium risk; if it is
In high windAnd (5) risking.
Thus, by setting two risk thresholds
And
i.e. the initial risk value R of the road section j can be assignedjThe classification is into three different grades of low risk, medium risk and high risk.
Wherein two risk thresholds are set
And
but is one specific embodiment of the present application. According to actual requirements, setting one or more than three risk thresholds is feasible, so that the initial risk value R of the road section j can be setjDivision into fewer or more different levels; for example, if R is set a risk thresholdjLess than or equal to the risk threshold, then classified as low risk, if RjAbove the risk threshold, a high risk is classified, thus classifying the risk into two different classes, low risk and high risk.
Optionally, the step of setting a dynamic update frequency of the real-time planned path information according to the road segment risk level includes: the dynamic update frequency is updated faster when the risk level is higher.
Thus, the update frequency updates faster when the risk level corresponding to the segment is higher, and slower when the risk level corresponding to the segment is lower. Therefore, the updating frequency can be set in a reasonable range, so that the remote processor is effectively utilized, the data calculation amount is reduced, and the path is prevented from being updated frequently; and because different risk road sections are divided, the problem that the path optimization is not timely caused by timing updating or the problem that the path optimization is too frequent or real-time updating is avoided.
Optionally, when the risk level is divided into three different levels, namely a low risk level, a medium risk level and a high risk level, the step of dynamically updating the frequency includes:
step 4-1, assuming that the current dangerous waste transport vehicle runs on the road section j, reading the length and the speed limit of the road section j in the initial planned path and the risk level of the next road section j +1, and calculating the slave node cj-1Reach next target feasible node cjThe required time Δ t;
step 4-2, presetting road condition information and updating frequency eta of real-time planned path according to risk level of the road section j +1 and driving time delta t of the road section jjThe process is as follows:
if the risk level of the road section j +1 is low, the updating frequency eta of the road section is setjIs Δ t;
if the risk level of the road section j +1 is middle, setting the updating frequency eta of the road sectionjIs Δ t/A, wherein A > 1;
if the risk level of the road section j +1 is high, the updating frequency eta of the road section is setjIs Deltat/B, wherein B > A.
Wherein, the delta t can be obtained by dividing the length of the road section j by the highest speed of the speed limit, and the frequency eta is updatedjThe unit of (c) is time/time.
Thus, when the risk level of the segment j +1 is low, due to the update frequency ηjΔ t, it is updated only once on segment j or again upon reaching the next intermediate node; when the risk level of the road section j +1 is medium or high, updating is carried out at least once; exemplarily, a ═ 2 and B ═ 3. Based on this, the dynamic update frequency can be combined with the risk value of the road segment, and the principle is that when the risk value of the next road segment is higher, the planned path needs to be recalculated more frequently, so as to ensure whether the current path planning is still the path with the minimized risk.
Likewise, when the risk level is classified into two different levels or more than three different levels, the dynamic update frequency may be set with reference to the classification into three different levels of low risk, medium risk, and high risk.
Optionally, the process of acquiring the dynamic parameter information of the hazardous waste transport vehicle in the driving process according to the dynamic update frequency is as follows: and (4) acquiring the driving behavior information and the traffic information of the adjacent road sections in real time when a specified updating time point is reached according to the updating frequency preset in the step (4).
Therefore, the dynamic parameter information is calculated based on the driving behavior information and the real-time traffic information, namely the driving behavior and the real-time traffic information can be brought into the risk assessment function model, the defect that only static parameters are collected is avoided, the real-time transportation risk is calculated more perfectly, and therefore the path is optimized.
Optionally, the driving behavior information includes at least one of a vehicle speed, an accumulated travel time, an acceleration, and an illegal stay time. It is understood that more parameters of the driving behavior information may be provided, and the more parameters, the more the driving behavior information may be provided, the more the dynamic risk value may be calculated from the parameters, and the driving behavior information may include the vehicle speed, the accumulated travel time, the acceleration, and the illegal parking time. The driving behavior information can be acquired in real time based on the technology of the internet of things by installing a vehicle-mounted GPS, a sensor, a camera and the like on the transport vehicle.
Optionally, the traffic information of the adjacent road segment includes at least one of a traffic flow, a congestion condition (predicted transit time), and a traffic accident condition (emergency accident). It is understood that the traffic information of the adjacent road segments may have more parameters, and the more the parameters are, the more the parameters are favorable for accurately calculating the dynamic risk value from the parameters, and as an embodiment, the traffic information of the adjacent road segments includes the traffic flow, the congestion condition, and the traffic accident condition. The traffic information of the adjacent road sections can be acquired in real time by means of arranging cameras at key road sections and nodes, connecting traffic system data online platforms and the like.
Optionally, the step of determining a dynamic correction coefficient of the probability of the occurrence of the risk accident according to the dynamic parameter is as follows:
let the dynamic correction coefficient of the link j be αj，αjIs defined as follows:
αj＝g(v,t,a,u,q,c,f) (4)
in the equation (4), v is a vehicle speed, t is an accumulated travel time, a is an acceleration, u is an illegal stay time, q is a traffic flow, c is a traffic jam, and f is a traffic accident.
With respect to alphajThe specific calculation method of the function can be based on an expert scoring method and a historical accident situation database, index weights of all parameters are distributed, and then weighting calculation is carried out.
E.g. alphaj＝0.1v+0.1t+0.1a+0.2u+0.15q+0.15c+0.2f；
Of course, above αjIn the calculation method of the function, specific parameter selection and parameter weight need to be determined by combining actual transportation information of the hazardous waste.
The above pair of dynamic correction coefficients alphajIn the definition formula, the parameters comprehensively introduced comprise the speed, the accumulated running time, the acceleration, the illegal staying time, the traffic flow, the congestion condition and the traffic accident condition of the transport vehicle positioned on the road section j.
Optionally, the step of importing dynamic parameters on the basis of the initial risk evaluation function model, constructing a dynamic risk evaluation function model, and calculating a real-time dynamic risk value of each road segment includes:
dynamic Risk value Hj，HjThe calculation formula of (a) is as follows:
Hj＝F(Pj,Qj) (5)
in the formula (5), PjProbability of occurrence of a risk accident for road section j, QjAs a consequence of an accident for section j;
the P isjAnd QjIs defined as follows:
Pj＝f(αj,l,d,n,w,p) (6)
in the formula (6), αjThe dynamic correction coefficient, the length of a road section, the road type, the number of lanes, the road width and the historical traffic accident rate are respectively represented by l, d, w and p;
in the formula (7), r is the danger level of dangerous waste, T is the weather condition, K is the temperature, rho is the human mouth density, and mu is other environmental influence factors.
Wherein the dynamic risk value HjWith an initial risk value RjThe functions of (a) are all in the same form, and are different in that: initial risk value RjThe dynamic correction factor alpha assumes a fixed value of 1 (i.e. corresponding to not taking into account the dynamic parameters) when the transport vehicle has not started yet, while the dynamic risk value HjThe dynamic parameters must be considered.
Optionally, the step of performing real-time transportation risk assessment so as to adjust the initial optimal path includes: and (4) carrying out real-time path planning weighting by adopting a Dijkstra algorithm to determine a real-time optimal path.
After the real-time transportation risk assessment is carried out, the risk grade division still needs to be carried out on the dynamic risk, if the risk grade assessment of the next road section is low, the Dijkstra algorithm does not need to be adopted for carrying out real-time path planning weighting, and if the risk grade assessment of the next road section is medium or high, the Dijkstra algorithm needs to be adopted for carrying out real-time path planning weighting. That is, if the risk level of the next road segment is evaluated as low, the path does not need to be re-planned, which is equivalent to the real-time path and the initial path remaining consistent.
Optionally, the step of performing real-time path planning weighting by using Dijkstra algorithm to determine a real-time optimal path includes:
step 6-3-1, initializing set S to include only next node c of road segment jjThe set E contains the remaining nodes, Rj][k]Is cjTo ckA path risk value of (a);
step 6-3-2, initialize node cjTo node c in set EkPath risk value dist k]＝R[j][k]. If there is no communication between nodes, dist [ k ]]＝∞；
Step 6-3-3, the minimum path risk value dist [ kmin [ ]]Corresponding node ckminMoving from the set E to the set S;
step 6-3-4, update node cjTo node c in set EkPath risk value of, dist k]＝min{dist[k],dist[kmin]+R[kmin][k]}；
6-3-5, if the target node is added into the set S, the updated optimal path is the adding sequence of the nodes in the set S; otherwise, go to step 6-3-3.
And then, the real-time optimal path plan is sent to the vehicle-mounted navigation according to the preset updating frequency, and the vehicle can adjust the driving route in time according to the guidance.
Has the advantages that:
(1) risk factors are brought into the optimal path planning process, so that the planned path is more comprehensive and objective;
(2) based on the technology of the Internet of things, dynamic parameter information in the driving process is acquired in real time, a new optimal path is synchronously planned in real time according to changes of environment or traffic conditions, and the accuracy and the real-time performance of the planned path are guaranteed;
(3) and the dynamic updating frequency of the route is set based on the risk level, the data calculation amount is reduced, the running load of a remote processor is effectively relieved, the frequent updating of the route is avoided, and the scientificity and the high efficiency of route planning are ensured.
Drawings
Certain specific embodiments of the present application will hereinafter be described in detail by way of example and not limitation with reference to the accompanying drawings, in which like reference numerals identify the same or similar parts or features, and it will be appreciated by those skilled in the art that the drawings are not necessarily drawn to scale. In the drawings:
FIG. 1 illustrates a method for routing in one embodiment of the present application;
FIG. 2 is a schematic illustration of a transport vehicle before a transport task is to be performed;
FIG. 3 is a schematic diagram of step 1 of a transport vehicle performing path planning;
FIG. 4 is a schematic diagram of step 2 of a transport vehicle performing path planning;
FIG. 5 is a schematic view of step 5 of a transport vehicle performing path planning;
FIG. 6 is a graph illustrating a determined real-time optimal planned path for a transport vehicle in one embodiment;
fig. 7 is a schematic diagram of risk assessment index parameter information according to an embodiment.
Detailed Description
In order that the manner in which the above-recited objects, features and advantages of the present application are obtained will be readily apparent, a more particular description of the invention briefly described above will be rendered by reference to specific details that are set forth in the appended description, which are indicative of but are capable of being practiced in a variety of ways other than those specifically described herein, and which are readily apparent to those of ordinary skill in the art, and which are therefore not limited to the specific embodiments disclosed below.
The technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.
Referring to fig. 1, an embodiment of the present application provides a path planning method, which may include: step 1 to step 6.
And 2, acquiring static parameter information of each segment between adjacent nodes, establishing a static risk evaluation function model based on the static parameter information, and performing preliminary path planning by adopting an improved Dijkstra algorithm to obtain an initial optimal path.
And 3, establishing a static risk evaluation function model in the step 2, calculating an initial risk value of each road section, and judging the static risk level of the road section according to the initial risk value.
And 4, determining the dynamic updating frequency of the real-time planning path information according to the road section risk level obtained in the step 3.
And 5, acquiring dynamic parameter information of the road section between the adjacent nodes when the updating time is up according to the dynamic updating frequency set in the step 4.
And 6, determining a dynamic correction coefficient of the accident probability according to the dynamic parameters obtained in the step 5, establishing a dynamic risk evaluation function model, calculating a real-time dynamic risk value of each road section, judging the dynamic risk level of the next road section, continuing to drive according to the initial path if the dynamic risk level is low risk, taking the node j corresponding to the road section j +1 as an initial node for updating the path plan if the dynamic risk level is increased to medium risk or high risk, performing real-time path plan by using an improved Dijkstra algorithm, screening a real-time optimal path, performing optimal path plan update by vehicle navigation, and adjusting the path.
The path planning method of the present application will be described in detail below with reference to fig. 2 to 6. The process of path planning may be performed by a remote server connected to the delivery vehicle, and for convenience of description, the subject of path planning is described below in terms of delivery vehicles.
As shown in fig. 2, 4 blocks represent buildings (or objects that other vehicles cannot pass through), and five stars represent hazardous waste transport vehicles, for the schematic view before the transport vehicles wait to perform the transport task.
As shown in fig. 3, a schematic view of step 1 of path planning for a transport vehicle. At this time, based on the electronic traffic map, the transport vehicle acquires the position and number of each node, wherein the node 1 is a starting node, the node 9 is a target node, and based on the position information of the starting node 1 and the target node 9, determines a selection area (such as in the same administrative area) of the intermediate node and acquires all intersection information in the selection area, acquires 7 intermediate nodes marked from 2 to 8 in total, and acquires static parameter information of each road section between adjacent nodes.
As shown in fig. 4, a schematic diagram of step 2 of path planning for a transport vehicle. At the moment, the transport vehicle collects static parameter information of all road sections between adjacent nodes, a static risk assessment function model is established based on the static parameter information of all the road sections, preliminary path planning weighting is carried out by utilizing a Dijkstra algorithm, an initial optimal path is screened, the initial optimal path sequentially passes through an intermediate node 2, an intermediate node 5 and an intermediate node 6 from an initial node 1 to reach a target node 9, and the road sections among all the nodes are respectively marked as a road section 1, a road section 2, a road section 3 and a road section 4.
As shown in fig. 5, a schematic diagram of step 5 of path planning for a transport vehicle. Before, the transport vehicle already determines the dynamic updating frequency based on the static risk level, and when the transport vehicle travels to the road section 1 and reaches the time node needing dynamic updating, the dynamic parameter information corresponding to each road section is acquired at the moment. Of course, the dynamically updated time node between nodes 1 and 2 shown in fig. 5 is just one embodiment, and the dynamically updated time node may be at node 2.
Then, the transport vehicle determines a dynamic correction coefficient of the accident probability based on the dynamic parameter information, establishes a dynamic risk evaluation function model, calculates a real-time dynamic risk value of each road section, and judges the dynamic risk level of the next road section (road section 2).
If the dynamic risk level for road segment 2 is identified as low risk, then no path re-planning is required and the transport vehicle still plans to travel along the path for road segment 2, road segment 3, and road segment 4 before the next dynamically updated time node.
If the dynamic risk level of the road segment 2 is determined to be medium risk or high risk, the transport vehicle may perform real-time path planning using the modified Dijkstra algorithm (when the road segment 2 is already excluded from the road segments of the real-time path planning), and filter the real-time optimal path. When a real-time path is planned, except for the road section 2, all road sections among nodes are subjected to real-time dynamic risk values, including the road section 1 which is already traveled by a vehicle, at the moment, dynamic parameters of all the road sections need to be brought in when the real-time dynamic risk values are calculated, and the dynamic risk values become the basis for dividing risk levels and determining dynamic updating frequency.
As shown in fig. 6, a real-time optimal planned path is determined for a transport vehicle. At this time, the intermediate node 2 becomes the starting node of the optimal planned path from the starting node 2 to the target node 9 through the intermediate node 3 and the intermediate node 6, and new planned road segments between the nodes are respectively marked as the road segments 5 and 6. And then, the vehicle-mounted navigation updates the optimal planned path and adjusts the route.
Fig. 7 is a schematic diagram of risk assessment index parameter information according to an embodiment of the present application. The risk evaluation index parameters include static parameter information and dynamic parameter information.
Taking the transportation of hazardous wastes as an example, the static parameter information comprises three types of hazardous waste vehicle information, road information and environmental information; the dynamic parameters comprise driving behavior information and traffic information of adjacent road sections.
Wherein the hazardous waste vehicle information includes hazardous waste type, hazardous waste characteristic, and vehicle approved load; the road information comprises road section length, road type, lane number, road width, road limitation and historical traffic accident rate; the environmental information includes weather, temperature, population density, and sensitive areas.
Wherein the driving behavior information includes a vehicle speed, an accumulated travel time, and an acceleration; the traffic information includes traffic flow, estimated transit time (congestion situation), and traffic accident situation.
Calculating accident consequences according to the hazardous waste vehicle information and the environmental information, and calculating accident probability according to the road information; and calculating dynamic correction parameters according to the driving behavior information and the traffic information so as to correct the accident probability and finally calculate the dangerous waste transportation risk value.
In summary, the embodiment of the application provides a method for planning a hazardous waste transportation path in real time based on risk minimization, risk factors are brought into an optimal path planning process, hazardous waste vehicles, road parameters, environmental parameters, traffic information and driving behaviors are comprehensively considered, a transportation risk assessment function model is built, risks of hazardous waste transportation are finely assessed, and the planned path is more comprehensive and objective. Based on the technology of the Internet of things, dynamic parameter information in the driving process is acquired in real time, a new optimal path is synchronously planned in real time according to changes of environment or traffic conditions, and the accuracy and the real-time performance of the planned path are guaranteed; and the dynamic updating frequency of the route is set based on the risk level, the data calculation amount is reduced, the running load of a remote processor is effectively relieved, the frequent updating of the route is avoided, and the scientificity and the high efficiency of route planning are ensured.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the present application have been illustrated and described in detail herein, many other variations and modifications consistent with the principles of the application may be ascertained or derived directly from the disclosure herein without departing from the spirit and scope of the application. Accordingly, the scope of the present application should be understood and interpreted to cover all such other variations or modifications.
Claims (10)
1. A method for risk minimization based real-time planning of hazardous waste transport paths, the method comprising:
step 1, acquiring position information of an initial node and a target node of a planned path, selecting a middle node of the planned path, and acquiring static parameter information of each road section between adjacent nodes;
step 2, establishing a static risk evaluation function model by taking the collected static parameters as evaluation indexes, calculating initial risk values of all road sections, performing preliminary path planning weighting by adopting a Dijkstra algorithm, and screening an initial optimal path;
step 3, according to the initial risk value, carrying out road section risk grade division;
step 4, setting dynamic updating frequency of real-time planning path information according to the road section risk level;
step 5, acquiring dynamic parameter information of the hazardous waste transport vehicle in the running process according to the dynamic updating frequency;
and 6, determining a dynamic correction coefficient of the probability of the occurrence of the risk accident according to the dynamic parameters, importing the dynamic parameters on the basis of the initial risk evaluation function model, constructing a dynamic risk evaluation function model, calculating a real-time dynamic risk value of each road section, and carrying out real-time transportation risk evaluation, thereby adjusting the initial optimal path.
2. The method of claim 1, wherein the step of selecting an intermediate node of the planned path comprises:
and acquiring the quantity and position information of the intersections based on the electronic traffic map, and taking each intersection as an intermediate node.
3. The method of claim 1, wherein the static parameter information comprises: information of hazardous waste transport vehicles, road information and environmental information;
wherein the information of the hazardous waste transport vehicle includes at least one of a category and hazardous characteristics of carrying hazardous waste, and a maximum load capacity of the vehicle;
the road information comprises at least one of length of a road section, road type, number of lanes, road width, historical traffic accident occurrence rate and road limit information;
the environmental information includes at least one of weather, temperature, population density, and environmental restriction information.
4. The method of claim 1, wherein the initial risk value is denoted as Rj，RjThe calculation formula of (a) is as follows:
Rj＝F(Pj,Qj) (1)
in the formula (1), PjProbability of occurrence of a risk accident for road section j, QjAs a consequence of an accident for section j;
the P isjAnd QjIs defined as follows:
Pj＝f(α,l,d,n,w,p) (2)
in the formula (2), alpha is a dynamic correction coefficient, l is the length of a road section, d is a road type, n is the number of lanes, w is a road width, and p is a historical traffic accident occurrence rate;
in the formula (3), r is the danger level of dangerous waste, T is the weather condition, K is the temperature, rho is the human mouth density, and mu is other environmental influence factors.
5. The method of claim 1, wherein the step of performing preliminary path planning weighting using Dijkstra's algorithm and screening the initial optimal path comprises:
step 2-2-1, the initialization set S contains only the start node c0The set E contains the remaining nodes, R [0 ]][k]Is c0To ckA path risk value of (a);
step 2-2-2, initialize the start node c0To node c in set EkPath risk value dist k]＝R[0][k]If the nodes are not connected, dist [ k ]]＝∞；
Step 2-2-3, the minimum path risk value dist [ kmin [ ]]Corresponding node ckminMoving from the set E to the set S;
step 2-2-4, update the initial node c0To node c in set EkPath risk value of, dist k]＝min{dist[k],dist[kmin]+R[kmin][k]}；
Step 2-3-5, if the target node is added into the set S, the optimal path is the adding sequence of the nodes in the set S; otherwise, go to step 2-2-2.
6. The method of claim 1, wherein the step of segment risk ranking according to the initial risk value comprises:
presetting a dangerous waste transportation risk threshold; and comparing the initial risk value with the risk threshold value, so as to carry out risk classification on each road section.
7. The method of claim 6, wherein the predetermined hazardous waste transport risk threshold; comparing the initial risk value with the risk threshold value, so as to perform risk classification on each road segment, wherein the step of performing risk classification on each road segment comprises the following steps:
step 3-1, presetting a dangerous waste transportation risk threshold value
And
step 3-2, according to the road section j obtained in the step 2Initial risk value R ofjCarrying out risk classification on each road section, and inputting a risk grade result into a road section set J; if it is
Classification as low risk; if it is
Is a medium risk; if it is
Is a high risk.
8. The method of claim 1, wherein the step of setting a dynamic update frequency of the real-time planned path information according to the link risk level comprises: the dynamic update frequency is updated faster when the risk level is higher.
9. The method of claim 1, wherein the step of obtaining dynamic parameter information during the driving of the hazardous waste transport vehicle according to the dynamic update frequency comprises the following steps:
according to the updating frequency preset in the step 4, when a specified updating time point is reached, acquiring driving behavior information and traffic information of adjacent road sections in real time;
the driving behavior information includes at least one of a vehicle speed, an accumulated travel time, an acceleration, and an illegal stay time;
the traffic information of the adjacent road section comprises at least one of traffic flow, congestion condition and traffic accident condition.
10. The method according to claim 1, wherein the step of determining a dynamic modification factor for the probability of the occurrence of the risk accident based on the dynamic parameters is as follows:
let the dynamic correction coefficient of the link j be αj，αjIs defined as follows:
αj＝g(v,t,a,u,q,c,f) (4)
in the equation (4), v is a vehicle speed, t is an accumulated travel time, a is an acceleration, u is an illegal stay time, q is a traffic flow, c is a traffic jam, and f is a traffic accident.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110624298.3A CN113361997A (en) | 2021-06-04 | 2021-06-04 | Dangerous waste transportation path real-time planning method based on risk minimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110624298.3A CN113361997A (en) | 2021-06-04 | 2021-06-04 | Dangerous waste transportation path real-time planning method based on risk minimization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113361997A true CN113361997A (en) | 2021-09-07 |
Family
ID=77532156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110624298.3A Pending CN113361997A (en) | 2021-06-04 | 2021-06-04 | Dangerous waste transportation path real-time planning method based on risk minimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113361997A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642811A (en) * | 2021-10-12 | 2021-11-12 | 氢山科技有限公司 | Dynamic hydrogen energy freight route planning method and device and computer equipment |
| CN114186755A (en) * | 2021-12-20 | 2022-03-15 | 中通服供应链管理有限公司 | Visual intelligent logistics dynamic optimization management and control method and system |
| CN114368386A (en) * | 2022-01-11 | 2022-04-19 | 恒安嘉新（北京）科技股份公司 | Early warning method, device, equipment and storage medium for vehicle safety |
| CN114529088A (en) * | 2022-02-21 | 2022-05-24 | 山东大学 | Driving path planning method and system based on accident risk cost |
| CN115271259A (en) * | 2022-09-22 | 2022-11-01 | 交通运输部水运科学研究所 | Positioning technology-based dangerous cargo container safety risk management method |
| CN115265569A (en) * | 2022-07-13 | 2022-11-01 | 上海外高桥保税区环保服务有限公司 | Hazardous waste transportation monitoring method and system, storage medium and intelligent terminal |
| CN116070983A (en) * | 2023-03-28 | 2023-05-05 | 交通运输部水运科学研究所 | Dangerous cargo container safety dispatching method, system, equipment and storage medium |
| CN116542592A (en) * | 2023-07-03 | 2023-08-04 | 深圳市华运国际物流有限公司 | International logistics transportation path analysis and assessment system |
| CN116579619A (en) * | 2023-07-13 | 2023-08-11 | 万联易达物流科技有限公司 | Air control method and system for freight bill |
| CN117391266A (en) * | 2023-09-14 | 2024-01-12 | 南通大学 | Muck truck path optimization method considering user satisfaction and safety risk |
| CN117474344A (en) * | 2023-12-28 | 2024-01-30 | 青岛盈智科技有限公司 | Risk assessment method and system for cargo transportation process |
| CN118464055A (en) * | 2024-07-10 | 2024-08-09 | 四川国蓝中天环境科技集团有限公司 | Muck truck transportation path planning method based on multi-objective optimization |
| CN118657467A (en) * | 2024-08-09 | 2024-09-17 | 朝阳辽西人防工程防护设施有限公司 | Material transfer vehicle path planning method based on visual perception and special vehicle |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100332436A1 (en) * | 2009-06-29 | 2010-12-30 | International Business Machines Corporation | Multi-pairs shortest path finding method and system |
| CN104156840A (en) * | 2014-07-29 | 2014-11-19 | 江汉大学 | Hazardous chemical transport route planning method and device |
| CN109886568A (en) * | 2019-02-01 | 2019-06-14 | 环境保护部华南环境科学研究所 | A kind of harmful influence road transport basin water environment methods of risk assessment and system |
| CN111091329A (en) * | 2019-12-18 | 2020-05-01 | 北京化工大学 | Semi-open type vehicle path optimization method for multi-vehicle-type transportation of hazardous chemical substances |
-
2021
- 2021-06-04 CN CN202110624298.3A patent/CN113361997A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100332436A1 (en) * | 2009-06-29 | 2010-12-30 | International Business Machines Corporation | Multi-pairs shortest path finding method and system |
| CN104156840A (en) * | 2014-07-29 | 2014-11-19 | 江汉大学 | Hazardous chemical transport route planning method and device |
| CN109886568A (en) * | 2019-02-01 | 2019-06-14 | 环境保护部华南环境科学研究所 | A kind of harmful influence road transport basin water environment methods of risk assessment and system |
| CN111091329A (en) * | 2019-12-18 | 2020-05-01 | 北京化工大学 | Semi-open type vehicle path optimization method for multi-vehicle-type transportation of hazardous chemical substances |
Non-Patent Citations (3)
| Title |
|---|
| 徐文星: "基于交通服务水平的危化品运输实时路径规划", 《化工学报》 * |
| 杜衡吉: "Dijkstra算法设计与实现", 《电脑知识与技术》 * |
| 沈小燕: "道路危险货物运输风险分析及路线优化研究", 《中国博士学位论文全文数据库》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642811B (en) * | 2021-10-12 | 2021-12-24 | 氢山科技有限公司 | Dynamic hydrogen energy freight route planning method and device and computer equipment |
| CN113642811A (en) * | 2021-10-12 | 2021-11-12 | 氢山科技有限公司 | Dynamic hydrogen energy freight route planning method and device and computer equipment |
| CN114186755A (en) * | 2021-12-20 | 2022-03-15 | 中通服供应链管理有限公司 | Visual intelligent logistics dynamic optimization management and control method and system |
| CN114368386B (en) * | 2022-01-11 | 2024-07-02 | 恒安嘉新（北京）科技股份公司 | Early warning method, device, equipment and storage medium for vehicle safety |
| CN114368386A (en) * | 2022-01-11 | 2022-04-19 | 恒安嘉新（北京）科技股份公司 | Early warning method, device, equipment and storage medium for vehicle safety |
| CN114529088A (en) * | 2022-02-21 | 2022-05-24 | 山东大学 | Driving path planning method and system based on accident risk cost |
| CN115265569A (en) * | 2022-07-13 | 2022-11-01 | 上海外高桥保税区环保服务有限公司 | Hazardous waste transportation monitoring method and system, storage medium and intelligent terminal |
| CN115271259A (en) * | 2022-09-22 | 2022-11-01 | 交通运输部水运科学研究所 | Positioning technology-based dangerous cargo container safety risk management method |
| CN116070983A (en) * | 2023-03-28 | 2023-05-05 | 交通运输部水运科学研究所 | Dangerous cargo container safety dispatching method, system, equipment and storage medium |
| CN116542592A (en) * | 2023-07-03 | 2023-08-04 | 深圳市华运国际物流有限公司 | International logistics transportation path analysis and assessment system |
| CN116542592B (en) * | 2023-07-03 | 2023-10-10 | 深圳市华运国际物流有限公司 | International logistics transportation path analysis and assessment system |
| CN116579619B (en) * | 2023-07-13 | 2023-09-22 | 万联易达物流科技有限公司 | Air control method and system for freight bill |
| CN116579619A (en) * | 2023-07-13 | 2023-08-11 | 万联易达物流科技有限公司 | Air control method and system for freight bill |
| CN117391266A (en) * | 2023-09-14 | 2024-01-12 | 南通大学 | Muck truck path optimization method considering user satisfaction and safety risk |
| CN117474344A (en) * | 2023-12-28 | 2024-01-30 | 青岛盈智科技有限公司 | Risk assessment method and system for cargo transportation process |
| CN117474344B (en) * | 2023-12-28 | 2024-03-22 | 青岛盈智科技有限公司 | Risk assessment method and system for cargo transportation process |
| CN118464055A (en) * | 2024-07-10 | 2024-08-09 | 四川国蓝中天环境科技集团有限公司 | Muck truck transportation path planning method based on multi-objective optimization |
| CN118657467A (en) * | 2024-08-09 | 2024-09-17 | 朝阳辽西人防工程防护设施有限公司 | Material transfer vehicle path planning method based on visual perception and special vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113361997A (en) | Dangerous waste transportation path real-time planning method based on risk minimization | |
| CN109415043B (en) | Automatic driving planning method based on comfort level | |
| JP5096396B2 (en) | Traffic information management server, navigation terminal and method | |
| US11022457B2 (en) | Method, apparatus, and computer program product for lane-level route guidance | |
| JP4980810B2 (en) | Traffic simulation apparatus and program | |
| JP6304104B2 (en) | Driving environment evaluation system | |
| EP2159777A2 (en) | Data mining to identify locations of potentially hazardous conditions for vehicle operation and use thereof | |
| CN110349420B (en) | Intelligent road condition management system based on data analysis | |
| CN110954119B (en) | Urban road motor vehicle optimal route induction method based on big data | |
| CN112801399B (en) | Path generation method and device, terminal equipment and storage medium | |
| CN114118795A (en) | Safety risk degree evaluation grading and dynamic early warning method for intelligent heavy-load expressway | |
| CN113743469A (en) | Automatic driving decision-making method fusing multi-source data and comprehensive multi-dimensional indexes | |
| CN113628446B (en) | Traffic information acquisition and analysis method and system based on Internet of things | |
| EP2587220A1 (en) | Method and device for providing an optimised route path to find a parking place in an area | |
| CN113808401A (en) | Traffic congestion prediction method, device, equipment and storage medium | |
| JP2007140745A (en) | Traffic congestion prediction system, traffic congestion factor estimation system, traffic congestion prediction method and traffic congestion factor estimation method | |
| JP2011113547A (en) | Traffic information estimation device, computer program for estimating traffic information, and traffic information estimation method | |
| Li et al. | Cloud aided safety-based route planning | |
| CN104331746B (en) | A kind of dynamic path optimization system and method for separate type | |
| Koramati et al. | Development of crash prediction model using artificial neural network (ANN): a case study of Hyderabad, India | |
| Sadeghi-Niaraki et al. | Multi-criteria decision-based model for road network process | |
| Cunha et al. | Genetic algorithm for the calibration of vehicle performance models of microscopic traffic simulators | |
| CN113408833A (en) | Public traffic key area identification method and device and electronic equipment | |
| JPH1079096A (en) | Device for providing path selection support information | |
| JP2004127104A (en) | Traffic information prediction system and program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210907 |
|
| RJ01 | Rejection of invention patent application after publication |